RRNS-convolutional encoded concatenated code for OFDM based wireless communication

The modern telecommunication industry demands higher capacity networks with high data rate. Orthogonal frequency division multiplexing (OFDM) is a promising technique for high data rate wireless communications at reasonable complexity in wireless channels. OFDM has been adopted for many types of wireless systems like wireless local area networks such as IEEE 802.11a, and digital audio/video broadcasting (DAB/DVB). The proposed research focuses on a concatenated coding scheme that improve the performance of OFDM based wireless communications. It uses a Redundant Residue Number System (RRNS) code as the outer code and a convolutional code as the inner code. The bit error rate (BER) performances of the proposed system under different channel conditions are investigated. These include the effect of additive white Gaussian noise (AWGN), multipath delay spread, peak power clipping and frame start synchronization error. The simulation results show that the proposed RRNS-Convolutional concatenated coding (RCCC) scheme provides significant improvement in the system performance by exploiting the inherent properties of RRNS.

Managing Scholarly Communication in the Web 2.0 Environment: The Role of University Libraries

Scholarly communication over the past 10 to 15 years has gained a tremendous momentum with the advent of Internet and the World Wide Web. The web has transformed the ways by which people search, find, use and communicate information. Innovations in web technology since 2005 have brought out an array of new services and facilities and an enhanced version of the web named Web 2.0. Web 2.0 facilitates a collaborative environment in which the information users can interact with the information. Web 2.0 enables its users to create, annotate, review, share re-use and represent the information in new ways thereby optimizing the information dissemination

Short Communication: Polystyrene-an inert carrier for L·glutaminase production by marine Vibrio costico/a under selld-state fermentation

Polystyrene beads, impregnated with mineral salts/glutamine medium as inert support, were used to produce L-glutaminase from Vibrio costicola by solid-state fermentation. Maximum enzyme yield, 88 U/g substrate, was after 36 h. Glucose at 10 g/kg enhanced the enzyme yield by 66%. The support system allowed glutaminase to be recovered with higher specific activity and lower viscosity than when a wheat-bran system was used

Rectangular Dielectric Resonator Antenna Configurations For Mobile Communication Applications

Experimental investigations on Microstrip line excited Dielectric Resonator Antenna configurations suitable for Mobile Communication applications are reported. High permittivity (εrd = 48) resonator samples with different aspect ratios are employed for the study. Theoretical analysis performed using FDTD method is also presented.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Biologically Plausible Neural Model for the Recognition of Biological Motion and Actions

The visual recognition of complex movements and actions is crucial for communication and survival in many species. Remarkable sensitivity and robustness of biological motion perception have been demonstrated in psychophysical experiments. In recent years, neurons and cortical areas involved in action recognition have been identified in neurophysiological and imaging studies. However, the detailed neural mechanisms that underlie the recognition of such complex movement patterns remain largely unknown. This paper reviews the experimental results and summarizes them in terms of a biologically plausible neural model. The model is based on the key assumption that action recognition is based on learned prototypical patterns and exploits information from the ventral and the dorsal pathway. The model makes specific predictions that motivate new experiments.

Computer-mediated communication and time pressure induce higher cardiovascular responses in the preparatory and execution phases of cooperative tasks

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10 easy steps to ease on-line communication

There are many different ways to communicate on-line now days from chat rooms, forums to e-mail, instant messaging, blogs and personal spaces. Some have clearly stated rules and some depend on unwritten codes of behaviour. Here are a few general tips provided that will hopefully make those occasions where learning and teaching are taking place more worthwhile.

Research and Communication Skills

A Collection of Material for Research and Communication Skills

A History of Communication

Speech, Writing, Print, Telephony, Web. How technology is catching up with the brain.

Tools for Communication, Collaboration �and for Mobile Learning

Introduction as part of UG TEL course